Integrated Gasification Combined Cycle (IGCC) technology continues progressing as an attractive technology for clean and efficient electric power generation, such as may be generated from abundant carbonaceous materials, e.g., coal and other relatively low-cost fuels. At the front end of IGCC is a process known as gasification, which is a partial oxidation process that transforms the fuel (e.g., coal) into a stream of combustible synthesis gas (syngas). IGCC is environmental-friendly because pollution-causing emissions (e.g., SOx, NOx, mercury, particulates, etc.) may be substantially removed from the syngas stream before combustion occurs. While IGCC technology intrinsically holds significant potential for clean and efficient power generation, there are opportunities yet to be exploited to improve IGCC power generation for enhanced reliability, availability, efficiency and flexibility.
It is known that present techniques for operation of an IGCC power plant tend to be based on simplistic control procedures, as may be conveyed to an operator by way of rigid and cumbersome operator guidelines, not necessarily designed to achieve any meaningful optimization strategy, such as may be due to limited online information for monitoring and controlling the IGCC plant. For example, a gasification section of the IGCC plant may be subject to a relatively harsh operating environment and as a result limited online sensors may be available for monitoring and control.
It is also known that model-based estimation implementations may be helpful to estimate plant variables. However, there are challenges that can arise since often modeling and/or sensing uncertainties may not be appropriately accounted for in such estimation implementations.
In view of the foregoing considerations, it would be desirable to formulate an estimation strategy where one may combine plant measurements, as may be obtained by way of a sensor suite, with model-based estimation for estimating plant variables, which are appropriately corrected for modeling and/or sensing uncertainties, without adding any substantial computational burden and while achieving substantial estimation accuracy.